Global convergence of a proximal linearized algorithm for difference of convex functions

被引:0
|
作者
João Carlos O. Souza
Paulo Roberto Oliveira
Antoine Soubeyran
机构
[1] Federal University of Rio de Janeiro,COPPE
[2] Federal University of Piauí,PESC
[3] Aix-Marseille University (Aix-Marseille School of Economics),CEAD
[4] CNRS and EHESS,undefined
来源
Optimization Letters | 2016年 / 10卷
关键词
Global optimization; Proximal linearized algorithm ; DC functions; Rate of convergence;
D O I
暂无
中图分类号
学科分类号
摘要
A proximal linearized algorithm for minimizing difference of two convex functions is proposed. If the sequence generated by the algorithm is bounded it is proved that every cluster point is a critical point of the function under consideration, even if the auxiliary minimizations are performed inexactly at each iteration. Linear convergence of the sequence is established under suitable additional assumptions.
引用
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页码:1529 / 1539
页数:10
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